class_docstr: Bag-level relation extraction dataset. Note that relation of NA should be named as 'NA'.
code: def __getitem__(self, index):
      bag = self.bag_scope[index]
      if self.bag_size > 0:
          if self.bag_size <= len(bag):
              resize_bag = random.sample(bag, self.bag_size)
          else:
              resize_bag = bag + list(
                  np.random.choice(bag, self.bag_size - len(bag))
              )
          bag = resize_bag
  
      seqs = None
      rel = self.rel2id[self.data[bag[0]]["relation"]]
      for sent_id in bag:
          item = self.data[sent_id]
          seq = list(self.tokenizer(item))
          if seqs is None:
              seqs = []
              for i in range(len(seq)):
                  seqs.append([])
          for i in range(len(seq)):
              seqs[i].append(seq[i])
      for i in range(len(seqs)):
          seqs[i] = torch.cat(
              seqs[i], 0
          )  # (n, L), n is the size of bag
      return [rel, self.bag_name[index], len(bag)] + seqs
  
imports: import torch
  import torch.utils.data
  import os, random, json, logging
  import numpy
  import sklearn.metrics
label_desc: Use image node_id 3 for calling the BagREDataset obj's underlying member method code with example usage: obj.__getitem__(index) and returns: unknown